Orthopaedic disorders are a leading cause of disability in the U.S., with arthritis and/or spine problems adversely affecting quality of life for more than 20% of adults. With an aging population, the rate of disability from orthopaedic disorders has been increasing steadily. While advances in diagnostic imaging (including CT, MRI and ultrasound) have greatly improved the ability to detect structural changes in musculoskeletal tissues, they typically reveal little about joint function. There is evidence that abnormal mechanical joint function contributes significantly to the development and progression of many types of joint disease. There is, therefore, a significant clinical need for the widespread use of technologies that can identify subtle abnormalities in joint function that, if left untreated, can compromise long-term joint health.
Biomechanical analyses are a key tool for providing quantitative objective measures of patient status and treatment outcomes. There are two key requirements for biomechanical assessment for orthopaedic injury/disease. First, the measurements must be relevant to the affected tissues (cartilage, ligaments, etc.). This requires a high level of accuracy, since deformations in the sub-mm range can be significant. Second, the measurements must be performed under physiological loading conditions, since the mechanical behavior of joints is highly nonlinear and dependent on muscle and external loads. This necessitates testing during functional activities relevant to the target population. At the heart of most in vivo biomechanical analyses is the estimation of the position and orientation (pose) of a multi-segment rigid body model based on recordings of 3D motion sensor data (optical, electromagnetic, or inertial).
Accurate in-vivo motion tracking is an important tool for understanding articulation kinematics, musculoskeletal related diseases and the effectiveness of different treatments. For example, to correlate abnormal motion with morphological features such as inter-vertebral disc height (<3 mm in posterior space), sub-millimeter accuracy is needed to avoid errors as large as 30% in disc-deformation measurements.
Model-based methods have been developed to measure 3D bone motion with high accuracy at knee or shoulder joints; such methods employ 3D models of the bones, which they track through a sequence of dynamic x-ray images. Radiographic model-based methods are more accurate than skin marker-based methods, which suffer from errors as large as 10 mm in translation and 8° in rotation. Model-based methods can also capture dynamic motion, unlike existing three-dimensional techniques such as Computed Tomography (CT, which also features higher radiation exposure, depending on the anatomic location) or Magnetic Resonance Imaging (MRI). Finally, unlike dynamic three-dimensional techniques (e.g., Cine-PC MRI), model-based methods: (a) do not require continuous movement for long periods of time during data collection, (b) support in general large ranges of motion, and (c) pose fewer restrictions during imaging (due to physical constraints imposed by CT and MRI imaging systems), thus leading to loadings more similar to most everyday movements. Given the advantages of model-based tracking, systems implementing model-based methods are utilized in various forms at several different academic institutions and medical research centers; the basic imaging hardware required for biplane radiography setup costs less than one third of what a modern 3T MRI scanner costs.
A conventional model-based tracking method has three major components: 2D projection image (Digitally Reconstructed Radiograph, DRR) generation; image processing; and optimization. DRRs are generated from a 3D bone model, acquired using standard, static 3D medical imaging techniques (CT or MRI). Image processing is applied to both DRR and X-ray images. Next, an optimization method tunes the position and orientation of the bone to find the best match between the DRR and the X-ray image. The process is repeated for all frames of a motion sequence. Multiple bones have to be tracked separately in the existing (i.e., single-bone) model-based methods. These single-bone tracking methods do not take into consideration differences between the actual radiographic images and the DRR due to bone overlap and/or implanted hardware, which limit their applicability to complex joints such as the spine.
Conventional implementations of 3D model-based tracking methods all suffer from the same critical issues. The existing tracking processes are extremely labor-intensive, requiring many (up to 30) hours of labor for every hour spent collecting data. Furthermore, a high level of expertise is required to generate trustworthy results. For this reason, the tracking task cannot be reliably outsourced or delegated to a crowd-sourcing approach such as the mechanical turk. Accuracy and reliability, especially for the more automated algorithms, are inconsistent and user-dependent. Simultaneous acquisition of a pair of radiographic images is a prerequisite for all systems claiming high 3D accuracy, but this requirement creates significant image quality problems due to scatter radiation (a widely known issue for biplane radiographic imaging, which can become intractable for imaging the thicker parts of the body such as hips or the lumbar spine). It is also often difficult or impossible to obtain two radiographic views that avoid bone overlap in the images, which also degrades imaging matching performance using conventional tracking approaches. Surgically inserted hardware further decreases tracking accuracy and robustness. These limitations have thus far restricted application of this technology to research studies, since the time and cost for data analysis is prohibitive for clinical use.